ELAIS-N2 master catalogue

Checks and diagnostics

In [1]:
from herschelhelp_internal import git_version
print("This notebook was run with herschelhelp_internal version: \n{}".format(git_version()))
This notebook was run with herschelhelp_internal version: 
44f1ae0 (Thu Nov 30 18:27:54 2017 +0000)
In [2]:
%matplotlib inline
#%config InlineBackend.figure_format = 'svg'

import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
plt.style.use('ggplot')

import locale
locale.setlocale(locale.LC_ALL, 'en_GB')

import os
import time
import itertools

from astropy.coordinates import SkyCoord
from astropy.table import Table
from astropy import units as u
from astropy import visualization as vis
import numpy as np
from matplotlib_venn import venn2

from herschelhelp_internal.masterlist import (nb_compare_mags, nb_ccplots, nb_histograms, find_last_ml_suffix,
                                              quick_checks)
In [3]:
OUT_DIR = os.environ.get('OUT_DIR', "./data")
SUFFIX = find_last_ml_suffix()
#SUFFIX = "20170710"

master_catalogue_filename = "master_catalogue_elais-n2_{}.fits".format(SUFFIX)
master_catalogue = Table.read("{}/{}".format(OUT_DIR, master_catalogue_filename))

print("Diagnostics done using: {}".format(master_catalogue_filename))
Diagnostics done using: master_catalogue_elais-n2_20171201.fits

0 - Quick checks

In [4]:
quick_checks(master_catalogue).show_in_notebook()
/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(184, False) will return an array of dtype('bool')
  format(shape, fill_value, array(fill_value).dtype), FutureWarning)
/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(184, 0) will return an array of dtype('int64')
  format(shape, fill_value, array(fill_value).dtype), FutureWarning)
Table shows only problematic columns.
Out[4]:
<Table length=49>
idxColumnAll nan#Measurements#Zeros#NegativeMinimum value
0f_ap_rcs_g1.00.00.00.00.0
1ferr_ap_rcs_g1.00.00.00.00.0
2m_ap_rcs_g1.00.00.00.00.0
3merr_ap_rcs_g1.00.00.00.00.0
4m_rcs_g0.0719237.00.0568.0-56.3566017151
5f_ap_rcs_r1.00.00.00.00.0
6ferr_ap_rcs_r1.00.00.00.00.0
7m_ap_rcs_r1.00.00.00.00.0
8merr_ap_rcs_r1.00.00.00.00.0
9f_ap_rcs_i1.00.00.00.00.0
10ferr_ap_rcs_i1.00.00.00.00.0
11m_ap_rcs_i1.00.00.00.00.0
12merr_ap_rcs_i1.00.00.00.00.0
13m_rcs_i0.0608769.00.0331.0-57.4051017761
14f_ap_rcs_z1.00.00.00.00.0
15ferr_ap_rcs_z1.00.00.00.00.0
16m_ap_rcs_z1.00.00.00.00.0
17merr_ap_rcs_z1.00.00.00.00.0
18m_rcs_z0.0549126.00.0443.0-58.0927009583
19f_ap_rcs_y1.00.00.00.00.0
20ferr_ap_rcs_y1.00.00.00.00.0
21m_ap_rcs_y1.00.00.00.00.0
22merr_ap_rcs_y1.00.00.00.00.0
23f_rcs_y1.00.00.00.00.0
24ferr_rcs_y1.00.00.00.00.0
25m_rcs_y1.00.00.00.00.0
26merr_rcs_y1.00.00.00.00.0
27ferr_ap_gpc1_g0.0218246.0102.00.00.0
28merr_ap_gpc1_g0.0218246.0102.00.00.0
29ferr_gpc1_g0.0218205.0137.00.00.0
30merr_gpc1_g0.0218205.0137.00.00.0
31ferr_ap_gpc1_r0.0226720.055.00.00.0
32merr_ap_gpc1_r0.0226720.055.00.00.0
33ferr_gpc1_r0.0226122.042.00.00.0
34merr_gpc1_r0.0226122.042.00.00.0
35ferr_ap_gpc1_i0.0228461.047.00.00.0
36merr_ap_gpc1_i0.0228461.047.00.00.0
37ferr_gpc1_i0.0228722.039.00.00.0
38merr_gpc1_i0.0228722.039.00.00.0
39ferr_ap_gpc1_z0.0227862.024.00.00.0
40merr_ap_gpc1_z0.0227862.024.00.00.0
41ferr_gpc1_z0.0227480.027.00.00.0
42merr_gpc1_z0.0227480.027.00.00.0
43ferr_ap_gpc1_y0.0223559.0142.00.00.0
44merr_ap_gpc1_y0.0223559.0142.00.00.0
45ferr_gpc1_y0.0221129.070.00.00.0
46merr_gpc1_y0.0221129.070.00.00.0
47f_ap_swire_irac10.0250360.00.01.0-29.81
48merr_ap_swire_irac10.0250360.00.01.0-0.0309585969153

I - Summary of wavelength domains

In [5]:
flag_obs = master_catalogue['flag_optnir_obs']
flag_det = master_catalogue['flag_optnir_det']
In [6]:
venn2(
    [
        np.sum(flag_obs == 1),
        np.sum(flag_obs == 4),
        np.sum(flag_obs == 5)
    ],
    set_labels=('Optical', 'mid-IR'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/len(flag_obs)))
)
plt.title("Wavelength domain observations");
In [7]:
venn2(
    [
        np.sum(flag_det[flag_obs == 5] == 1),
        np.sum(flag_det[flag_obs == 5] == 4),
        np.sum(flag_det[flag_obs == 5] == 5)
    ],
    set_labels=('Optical', 'Mid-IR'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/np.sum(flag_det != 0)))
)
plt.title("Detection of the {} sources detected\n in any wavelength domains "
          "(among {} sources)".format(
              locale.format('%d', np.sum(flag_det != 0), grouping=True),
              locale.format('%d', len(flag_det), grouping=True)));

II - Comparing magnitudes in similar filters

The master list if composed of several catalogues containing magnitudes in similar filters on different instruments. We are comparing the magnitudes in these corresponding filters.

In [8]:
u_bands = ["WFC u", "CFHT Megacam u"]
g_bands = ["WFC g", "CFHT Megacam g", "GPC1 g", "RCS g"]
r_bands = ["WFC r", "CFHT Megacam r", "GPC1 r", "RCS r"]
i_bands = ["WFC i",                   "GPC1 i", "RCS i"]
z_bands = ["WFC z", "CFHT Megacam z", "GPC1 z", "RCS z"]
y_bands = [                           "GPC1 y", "RCS y"]

II.a - Comparing depths

We compare the histograms of the total aperture magnitudes of similar bands.

In [9]:
for bands in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    colnames = ["m_{}".format(band.replace(" ", "_").lower()) for band in bands]
    nb_histograms(master_catalogue, colnames, bands)
HELP warning: the column m_rcs_y (RCS y) is empty.

II.b - Comparing magnitudes

We compare one to one each magnitude in similar bands.

In [10]:
for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    for band1, band2 in itertools.combinations(band_of_a_kind, 2):
        
        basecol1, basecol2 = band1.replace(" ", "_").lower(), band2.replace(" ", "_").lower()
        
        col1, col2 = "m_ap_{}".format(basecol1), "m_ap_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (aperture)".format(band1), "{} (aperture)".format(band2)))
        
        col1, col2 = "m_{}".format(basecol1), "m_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (total)".format(band1), "{} (total)".format(band2)))
CFHT Megacam u (aperture) - WFC u (aperture):
- Median: -0.04
- Median Absolute Deviation: 0.25
- 1% percentile: -0.8018979835510254
- 99% percentile: 3.7088455772399915
CFHT Megacam u (total) - WFC u (total):
- Median: -0.24
- Median Absolute Deviation: 0.20
- 1% percentile: -1.133989028930664
- 99% percentile: 2.9416127777099588
CFHT Megacam g (aperture) - WFC g (aperture):
- Median: 0.07
- Median Absolute Deviation: 0.16
- 1% percentile: -0.6488978195190429
- 99% percentile: 1.2106095886230488
CFHT Megacam g (total) - WFC g (total):
- Median: -0.06
- Median Absolute Deviation: 0.15
- 1% percentile: -1.013030776977539
- 99% percentile: 2.029975814819333
GPC1 g (aperture) - WFC g (aperture):
- Median: -0.04
- Median Absolute Deviation: 0.23
- 1% percentile: -1.936914939880371
- 99% percentile: 2.213923206329347
GPC1 g (total) - WFC g (total):
- Median: 0.03
- Median Absolute Deviation: 0.21
- 1% percentile: -2.2826806068420407
- 99% percentile: 1.6533357620239293
No sources have both WFC g (aperture) and RCS g (aperture) values.
RCS g (total) - WFC g (total):
- Median: -0.09
- Median Absolute Deviation: 0.17
- 1% percentile: -1.1273002624511719
- 99% percentile: 1.1714000701904297
GPC1 g (aperture) - CFHT Megacam g (aperture):
- Median: -0.14
- Median Absolute Deviation: 0.34
- 1% percentile: -3.5418136596679686
- 99% percentile: 2.190652847290034
GPC1 g (total) - CFHT Megacam g (total):
- Median: 0.08
- Median Absolute Deviation: 0.23
- 1% percentile: -3.406550827026367
- 99% percentile: 1.7025775146484374
No sources have both CFHT Megacam g (aperture) and RCS g (aperture) values.
RCS g (total) - CFHT Megacam g (total):
- Median: -0.03
- Median Absolute Deviation: 0.15
- 1% percentile: -1.1794805526733398
- 99% percentile: 1.0631583595275873
No sources have both GPC1 g (aperture) and RCS g (aperture) values.
RCS g (total) - GPC1 g (total):
- Median: -0.13
- Median Absolute Deviation: 0.25
- 1% percentile: -1.8683868789672853
- 99% percentile: 2.8599301910400383
CFHT Megacam r (aperture) - WFC r (aperture):
- Median: 0.06
- Median Absolute Deviation: 0.15
- 1% percentile: -0.6783035278320313
- 99% percentile: 1.7159394073486296
CFHT Megacam r (total) - WFC r (total):
- Median: -0.10
- Median Absolute Deviation: 0.14
- 1% percentile: -1.0879096031188964
- 99% percentile: 2.0215468215942365
GPC1 r (aperture) - WFC r (aperture):
- Median: -0.04
- Median Absolute Deviation: 0.13
- 1% percentile: -1.1710252380371093
- 99% percentile: 1.3535482025146466
GPC1 r (total) - WFC r (total):
- Median: 0.03
- Median Absolute Deviation: 0.12
- 1% percentile: -1.5002924537658693
- 99% percentile: 1.019995574951169
No sources have both WFC r (aperture) and RCS r (aperture) values.
RCS r (total) - WFC r (total):
- Median: -0.14
- Median Absolute Deviation: 0.14
- 1% percentile: -1.1533707427978517
- 99% percentile: 1.0309009552001953
GPC1 r (aperture) - CFHT Megacam r (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.22
- 1% percentile: -3.7211384391784668
- 99% percentile: 1.368954734802246
GPC1 r (total) - CFHT Megacam r (total):
- Median: 0.12
- Median Absolute Deviation: 0.12
- 1% percentile: -2.8720093154907227
- 99% percentile: 1.0834720611572262
No sources have both CFHT Megacam r (aperture) and RCS r (aperture) values.
RCS r (total) - CFHT Megacam r (total):
- Median: -0.05
- Median Absolute Deviation: 0.15
- 1% percentile: -1.153233814239502
- 99% percentile: 1.0873112678527819
No sources have both GPC1 r (aperture) and RCS r (aperture) values.
RCS r (total) - GPC1 r (total):
- Median: -0.17
- Median Absolute Deviation: 0.11
- 1% percentile: -1.151779556274414
- 99% percentile: 1.3511592864990298
GPC1 i (aperture) - WFC i (aperture):
- Median: 0.03
- Median Absolute Deviation: 0.12
- 1% percentile: -0.9274186611175538
- 99% percentile: 0.9799727630615245
GPC1 i (total) - WFC i (total):
- Median: 0.10
- Median Absolute Deviation: 0.11
- 1% percentile: -1.008834228515625
- 99% percentile: 0.8782679748535149
No sources have both WFC i (aperture) and RCS i (aperture) values.
RCS i (total) - WFC i (total):
- Median: -0.10
- Median Absolute Deviation: 0.18
- 1% percentile: -1.1100502014160156
- 99% percentile: 1.3543500900268555
No sources have both GPC1 i (aperture) and RCS i (aperture) values.
RCS i (total) - GPC1 i (total):
- Median: -0.23
- Median Absolute Deviation: 0.09
- 1% percentile: -0.8339004516601562
- 99% percentile: 0.630829010009765
CFHT Megacam z (aperture) - WFC z (aperture):
- Median: 0.14
- Median Absolute Deviation: 0.17
- 1% percentile: -0.6702371978759765
- 99% percentile: 3.5893981742858516
CFHT Megacam z (total) - WFC z (total):
- Median: -0.04
- Median Absolute Deviation: 0.20
- 1% percentile: -0.9459419250488281
- 99% percentile: 2.56119647979736
GPC1 z (aperture) - WFC z (aperture):
- Median: 0.04
- Median Absolute Deviation: 0.13
- 1% percentile: -0.8755176925659179
- 99% percentile: 1.1334119796752926
GPC1 z (total) - WFC z (total):
- Median: 0.08
- Median Absolute Deviation: 0.17
- 1% percentile: -0.9569845581054688
- 99% percentile: 1.4286836242675776
No sources have both WFC z (aperture) and RCS z (aperture) values.
RCS z (total) - WFC z (total):
- Median: -0.07
- Median Absolute Deviation: 0.22
- 1% percentile: -1.0420454406738282
- 99% percentile: 1.9726009368896484
GPC1 z (aperture) - CFHT Megacam z (aperture):
- Median: -0.06
- Median Absolute Deviation: 0.20
- 1% percentile: -3.8274253082275393
- 99% percentile: 1.0357662200927726
GPC1 z (total) - CFHT Megacam z (total):
- Median: 0.15
- Median Absolute Deviation: 0.11
- 1% percentile: -2.3263026237487794
- 99% percentile: 0.9511220550537027
No sources have both CFHT Megacam z (aperture) and RCS z (aperture) values.
RCS z (total) - CFHT Megacam z (total):
- Median: -0.05
- Median Absolute Deviation: 0.24
- 1% percentile: -2.377850933074951
- 99% percentile: 1.2616420936584478
No sources have both GPC1 z (aperture) and RCS z (aperture) values.
RCS z (total) - GPC1 z (total):
- Median: -0.18
- Median Absolute Deviation: 0.11
- 1% percentile: -1.082212905883789
- 99% percentile: 1.0015361785888657
No sources have both GPC1 y (aperture) and RCS y (aperture) values.
No sources have both GPC1 y (total) and RCS y (total) values.

III - Comparing magnitudes to reference bands

Cross-match the master list to SDSS magnitudes.

In [11]:
master_catalogue_coords = SkyCoord(master_catalogue['ra'], master_catalogue['dec'])

III.a - Comparing u, g, r, i, and z bands to SDSS

The catalogue is cross-matched to SDSS-DR13 withing 0.2 arcsecond.

We compare the u, g, r, i, and z magnitudes to those from SDSS using fiberMag for the aperture magnitude and petroMag for the total magnitude.

In [12]:
sdss = Table.read("../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_ELAIS-N2.fits")
sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)

idx, d2d, _ = sdss_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

sdss = sdss[mask]
ml_sdss_idx = idx[mask]
In [13]:
for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands]:
    for band in band_of_a_kind:
        
        sdss_mag_ap = sdss["fiberMag_{}".format(band[-1])]
        master_cat_mag_ap = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
    
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (fiberMag)".format(band[-1]), "{} (aperture)".format(band)))
    
        sdss_mag_tot = sdss["petroMag_{}".format(band[-1])]
        master_cat_mag_tot = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
        
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (petroMag)".format(band[-1]), "{} (total)".format(band)))
WFC u (aperture) - SDSS u (fiberMag):
- Median: -0.30
- Median Absolute Deviation: 0.18
- 1% percentile: -1.6183706665039062
- 99% percentile: 0.9621364212036125
WFC u (total) - SDSS u (petroMag):
- Median: -0.30
- Median Absolute Deviation: 0.18
- 1% percentile: -1.6183706665039062
- 99% percentile: 0.9621364212036125
CFHT Megacam u (aperture) - SDSS u (fiberMag):
- Median: -0.04
- Median Absolute Deviation: 0.58
- 1% percentile: -1.6583412170410157
- 99% percentile: 3.297002315521247
CFHT Megacam u (total) - SDSS u (petroMag):
- Median: -0.04
- Median Absolute Deviation: 0.58
- 1% percentile: -1.6583412170410157
- 99% percentile: 3.297002315521247
WFC g (aperture) - SDSS g (fiberMag):
- Median: -0.44
- Median Absolute Deviation: 0.11
- 1% percentile: -1.1331189155578614
- 99% percentile: 0.5049597740173323
WFC g (total) - SDSS g (petroMag):
- Median: -0.44
- Median Absolute Deviation: 0.11
- 1% percentile: -1.1331189155578614
- 99% percentile: 0.5049597740173323
CFHT Megacam g (aperture) - SDSS g (fiberMag):
- Median: -0.36
- Median Absolute Deviation: 0.18
- 1% percentile: -1.1016152000427246
- 99% percentile: 3.0158825111388934
CFHT Megacam g (total) - SDSS g (petroMag):
- Median: -0.36
- Median Absolute Deviation: 0.18
- 1% percentile: -1.1016152000427246
- 99% percentile: 3.0158825111388934
GPC1 g (aperture) - SDSS g (fiberMag):
- Median: -0.46
- Median Absolute Deviation: 0.20
- 1% percentile: -2.3214307403564454
- 99% percentile: 1.6727563476562555
GPC1 g (total) - SDSS g (petroMag):
- Median: -0.46
- Median Absolute Deviation: 0.20
- 1% percentile: -2.3214307403564454
- 99% percentile: 1.6727563476562555
No sources have both SDSS g (fiberMag) and RCS g (aperture) values.
No sources have both SDSS g (petroMag) and RCS g (total) values.
WFC r (aperture) - SDSS r (fiberMag):
- Median: -0.34
- Median Absolute Deviation: 0.07
- 1% percentile: -0.890346794128418
- 99% percentile: 0.36871064186096253
WFC r (total) - SDSS r (petroMag):
- Median: -0.34
- Median Absolute Deviation: 0.07
- 1% percentile: -0.890346794128418
- 99% percentile: 0.36871064186096253
CFHT Megacam r (aperture) - SDSS r (fiberMag):
- Median: -0.27
- Median Absolute Deviation: 0.13
- 1% percentile: -0.8717788696289063
- 99% percentile: 3.9546873378753666
CFHT Megacam r (total) - SDSS r (petroMag):
- Median: -0.27
- Median Absolute Deviation: 0.13
- 1% percentile: -0.8717788696289063
- 99% percentile: 3.9546873378753666
GPC1 r (aperture) - SDSS r (fiberMag):
- Median: -0.35
- Median Absolute Deviation: 0.11
- 1% percentile: -1.2985172271728516
- 99% percentile: 0.8457000732421677
GPC1 r (total) - SDSS r (petroMag):
- Median: -0.35
- Median Absolute Deviation: 0.11
- 1% percentile: -1.2985172271728516
- 99% percentile: 0.8457000732421677
No sources have both SDSS r (fiberMag) and RCS r (aperture) values.
No sources have both SDSS r (petroMag) and RCS r (total) values.
WFC i (aperture) - SDSS i (fiberMag):
- Median: -0.39
- Median Absolute Deviation: 0.08
- 1% percentile: -0.9288136291503907
- 99% percentile: 0.4154920196533197
WFC i (total) - SDSS i (petroMag):
- Median: -0.39
- Median Absolute Deviation: 0.08
- 1% percentile: -0.9288136291503907
- 99% percentile: 0.4154920196533197
GPC1 i (aperture) - SDSS i (fiberMag):
- Median: -0.34
- Median Absolute Deviation: 0.09
- 1% percentile: -1.0123353004455566
- 99% percentile: 0.48122777938842726
GPC1 i (total) - SDSS i (petroMag):
- Median: -0.34
- Median Absolute Deviation: 0.09
- 1% percentile: -1.0123353004455566
- 99% percentile: 0.48122777938842726
No sources have both SDSS i (fiberMag) and RCS i (aperture) values.
No sources have both SDSS i (petroMag) and RCS i (total) values.
WFC z (aperture) - SDSS z (fiberMag):
- Median: -0.33
- Median Absolute Deviation: 0.11
- 1% percentile: -1.2395364761352539
- 99% percentile: 0.7405158233642565
WFC z (total) - SDSS z (petroMag):
- Median: -0.33
- Median Absolute Deviation: 0.11
- 1% percentile: -1.2395364761352539
- 99% percentile: 0.7405158233642565
CFHT Megacam z (aperture) - SDSS z (fiberMag):
- Median: -0.19
- Median Absolute Deviation: 0.19
- 1% percentile: -1.0708820343017578
- 99% percentile: 3.6499409675597967
CFHT Megacam z (total) - SDSS z (petroMag):
- Median: -0.19
- Median Absolute Deviation: 0.19
- 1% percentile: -1.0708820343017578
- 99% percentile: 3.6499409675597967
GPC1 z (aperture) - SDSS z (fiberMag):
- Median: -0.29
- Median Absolute Deviation: 0.13
- 1% percentile: -1.2182876586914062
- 99% percentile: 0.9481426239013657
GPC1 z (total) - SDSS z (petroMag):
- Median: -0.29
- Median Absolute Deviation: 0.13
- 1% percentile: -1.2182876586914062
- 99% percentile: 0.9481426239013657
No sources have both SDSS z (fiberMag) and RCS z (aperture) values.
No sources have both SDSS z (petroMag) and RCS z (total) values.

Keeping only sources with good signal to noise ratio

From here, we are only comparing sources with a signal to noise ratio above 3, i.e. roughly we a magnitude error below 0.3.

To make it easier, we are setting to NaN in the catalogue the magnitudes associated with an error above 0.3 so we can't use these magnitudes after the next cell.

In [14]:
for error_column in [_ for _ in master_catalogue.colnames if _.startswith('merr_')]:
    column = error_column.replace("merr", "m")
    keep_mask = np.isfinite(master_catalogue[error_column])
    keep_mask[keep_mask] &= master_catalogue[keep_mask][error_column] <= 0.3
    master_catalogue[column][~keep_mask] = np.nan

IV - Comparing aperture magnitudes to total ones.

In [15]:
nb_ccplots(
    master_catalogue['m_cfht_megacam_r'],
    master_catalogue['m_ap_cfht_megacam_r'] - master_catalogue['m_cfht_megacam_r'],
    "r total magnitude (CFHT)", "r aperture mag - total mag (CFHT)",
    master_catalogue["stellarity"],
    invert_x=True
)
Number of source used: 764852 / 1783240 (42.89%)

V - Color-color and magnitude-color plots

In [16]:
nb_ccplots(
    master_catalogue['m_wfc_i'] - master_catalogue['m_swire_irac1'],
    master_catalogue['m_wfc_g'] - master_catalogue['m_wfc_i'],
    "WFC i - IRAC1", "g - i (WFC)",
    master_catalogue["stellarity"]
)
Number of source used: 102035 / 1783240 (5.72%)
In [17]:
nb_ccplots(
    master_catalogue['m_cfht_megacam_u'] - master_catalogue['m_cfht_megacam_g'],
    master_catalogue['m_cfht_megacam_g'] - master_catalogue['m_cfht_megacam_r'],
    "u - g (CFHT)", "g - r (CFHT)",
    master_catalogue["stellarity"]
)
Number of source used: 665469 / 1783240 (37.32%)
In [18]:
nb_ccplots(
    master_catalogue['m_irac3'] - master_catalogue['m_irac4'],
    master_catalogue['m_swire_irac1'] - master_catalogue['m_swire_irac2'],
    "IRAC3 - IRAC4", "IRAC1 - IRAC2",
    master_catalogue["stellarity"]
)
Number of source used: 19548 / 1783240 (1.10%)